




'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor




class 					_snake_case						(      unittest.TestCase				):




			def __init__(   self							,				a__							,				a__=7							,				a__=3							,				a__=18							,				a__=30							,				a__=400							,				a__=True							,				a__=None							,				a__=True							,				a__=None							,				)							->							List[str]:

										'''simple docstring'''




										snake_case_         = size if size is not None else {"shortest_edge": 20}
										snake_case_         = crop_size if crop_size is not None else {"height": 18, "width": 18}
										snake_case_         = parent
										snake_case_         = batch_size
										snake_case_         = num_channels
										snake_case_         = image_size
										snake_case_         = min_resolution
										snake_case_         = max_resolution
										snake_case_         = do_resize
										snake_case_         = size
										snake_case_         = do_center_crop
										snake_case_         = crop_size




			def   lowerCAmelCase__							(   self				)							->							str:

										'''simple docstring'''




										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class 					_snake_case						(      _UpperCAmelCase		,		unittest.TestCase				):
			lowerCAmelCase_	: Optional[int]					=       MobileNetVaImageProcessor if is_vision_available() else None




			def   lowerCAmelCase__							(   self				)							->							int:

										'''simple docstring'''




										snake_case_         = MobileNetVaImageProcessingTester(self				)




			@property
			def   lowerCAmelCase__							(   self				)							->							List[Any]:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def   lowerCAmelCase__							(   self				)							->							Any:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(a__							,				"do_resize"				)				)
										self.assertTrue(hasattr(a__							,				"size"				)				)
										self.assertTrue(hasattr(a__							,				"do_center_crop"				)				)
										self.assertTrue(hasattr(a__							,				"crop_size"				)				)




			def   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size							,				{"shortest_edge": 20}				)
										self.assertEqual(image_processor.crop_size							,				{"height": 18, "width": 18}				)

										snake_case_         = self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84				)
										self.assertEqual(image_processor.size							,				{"shortest_edge": 42}				)
										self.assertEqual(image_processor.crop_size							,				{"height": 84, "width": 84}				)




			def   lowerCAmelCase__							(   self				)							->							Tuple:

										'''simple docstring'''




										pass




			def   lowerCAmelCase__							(   self				)							->							Dict:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										snake_case_         = prepare_image_inputs(self.image_processor_tester							,				equal_resolution=a__				)
										for image in image_inputs:
																	self.assertIsInstance(a__							,				Image.Image				)

										# Test not batched input
										snake_case_         = image_processing(image_inputs[0]							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)

										# Test batched
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)




			def   lowerCAmelCase__							(   self				)							->							List[Any]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										snake_case_         = prepare_image_inputs(self.image_processor_tester							,				equal_resolution=a__							,				numpify=a__				)
										for image in image_inputs:
																	self.assertIsInstance(a__							,				np.ndarray				)

										# Test not batched input
										snake_case_         = image_processing(image_inputs[0]							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)

										# Test batched
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)




			def   lowerCAmelCase__							(   self				)							->							List[Any]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										snake_case_         = prepare_image_inputs(self.image_processor_tester							,				equal_resolution=a__							,				torchify=a__				)
										for image in image_inputs:
																	self.assertIsInstance(a__							,				torch.Tensor				)

										# Test not batched input
										snake_case_         = image_processing(image_inputs[0]							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)

										# Test batched
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values
										self.assertEqual(
										    encoded_images.shape							,				(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )							,				)











"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






